Segmentation and registration of 2D multiphoton microscopy images

Projecte realitzat en col.laboració amb el centre Purdue UniversityOptical microscopy exhibits many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, and characteristically have low signal levels. This thesis describes the initial work in segmenting and registering multiphoton fluorescent microscopy images via a combination of methods. In particular, it describes a method that utilizes image enhancement and spatial filtering along with registration (to correct translational motion) and temporal filtering. Experimental results indicate the methods are promising

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

Individualized learning and integration for multi-modality data

Individualized modeling and multi-modality data integration have experienced an explosive growth in recent years, which have many important applications in biomedical research, personalized education and marketing. Conventional statistical models usually fail to capture significant variation due to subject-specific effects and heterogeneity of data from multiple sources. Consequently, it has become very critical to incorporate individuals’ and modalities’ heterogeneous characteristics in order to efficiently explore the data structure and enhance the prediction power. In this thesis, we address three challenging issues: mixture modeling for longitudinal data, individualized variable selection and multi-modality tensor learning with an application in medical imaging analysis. In the first part of the thesis, we develop a model-based subgrouping method for longitudinal data. Specifically, we propose an unbiased estimating equation approach for a two-component mixture model with correlated response data. In contrast to most existing longitudinal data clustering methods, the proposed model allows subgroup membership change for each individual over time. Furthermore, we incorporate correlation structure on unobservable latent indicator variables. Another advantage our approach is that we do not require any information about joint likelihood function for each subject. The proposed model is shown to have more efficient parameter estimators in both mixing proportions and component densities. In addition, by utilizing within-subject serial correlations, the proposed approach enhances classification power compared to existing methods, especially for those boundary observations. In the second part of the thesis, we propose an individualized variable selection approach to select different relevant variables for different individuals. The conventional homogeneous model, which assumes all subjects share the same effects of certain predictors, may wash out important information due to heterogeneous variation. For example, in personalized medicine, some individuals could have positive responses to the treatment while some individuals could have negative ones. Hence the population average effect could be close to zero. In this thesis, we construct a separation penalty with multi-directional shrinkages including zero, which facilitates individualized modeling to distinguish strong signals from noisy ones. As a byproduct, the proposed model identifies subgroups among which individuals share similar effects, and thus improves estimation efficiency and personalized prediction accuracy. Finite sample simulation studies and an application to HIV longitudinal data demonstrate the model efficiency and the prediction power of the new approach compared to a variety of existing penalization models. In the third part of the thesis, we are interested in employing medical imaging data for diagnosis. This work is motivated by breast cancer imaging data produced by a multimodality multiphoton optical imaging technique. We develop an innovative multilayer tensor learning method to predict disease status effectively through utilizing subject-wise imaging information. In particular, we propose an individualized multilayer model which leverages an additional layer of individual structure of imaging shared by multiple modalities in addition to employing a high-order tensor decomposition shared by populations. One major advantage of our approach is that we are able to capture the spatial information of microvesicles observed in certain modalities of optical imaging through integrating multimodality imaging data. Our simulation studies and real data analysis both indicate that the proposed multilayer learning method improves prediction accuracy significantly compared to existing competitive statistical and machine learning methods

Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications

Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i.d and dynamic compressed sensing by considering such constraints. In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution. Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second

A New Representation for Spectral Data Applied to Raman Spectroscopy of Brain Cancer

Par sa nature infiltrative et son confinement derrière la barrière hémo-encéphalique, le cancer primaire du cerveau est l’une des néoplasies les plus difficiles à diagnostiquer et traiter. Son traitement repose sur la résection chirurgicale maximale. La spectroscopie Raman, capable d’identifier en temps réel des régions cancéreuses qui apparaîtraient normales à l’œil nu, promet d’améliorer considérablement le guidage neurochirurgical et maximiser la résection de la masse tumorale. Cependant, le signal Raman est très complexe à interpréter : les systèmes Raman peuvent maintenant capter des signaux de grande qualité que les méthodes analytiques actuelles ne parviennent pas à interpréter de manière reproductible. Ceci constitue une barrière importante à l’acceptation de la spectroscopie Raman par les médecins et les chercheurs œuvrant sur le cancer du cerveau. L’objectif de ce travail est de développer une méthode robuste d’ingénierie des variables (« Feature engineering ») qui permettrait d’identifier les processus moléculaires exploités par les systèmes Raman pour différentier les régions cancéreuses des régions saines lors de chirurgies cérébrales. Tout d’abord, nous avons identifié les régions Raman ayant une haute spécificité à notre problématique clinique par une revue systématique de la littérature. Un algorithme d’ajustement de courbe a été développé afin d’extraire la forme des pics Raman dans les régions sélectionnées. Puis, nous avons élaboré un modèle mathématique qui tient compte de l’interactivité entre les molécules de l’échantillon interrogé, ainsi qu’entre le signal Raman et l’âge du patient opéré. Pour valider le modèle, nous avons comparé sa capacité à compresser le signal avec celle de l’analyse en composante principale (ACP), le standard en spectroscopie Raman. Finalement, nous avons appliqué la méthode d’ingénierie des variables à des spectres Raman acquis en salle d’opération afin d’identifier quels processus moléculaires indiquaient la présence de cancer. Notre méthode a démontré une meilleure rétention d’information que l’ACP. En l’appliquant aux spectres Raman in vivo, les zones denses en cellules malignes démontrent une expression augmentée d’acides nucléiques ainsi que de certaines protéines, notamment le collagène, le tryptophan et la phénylalanine. De plus, l’âge des patients semble affecter l’impact qu’ont certaines protéines, lipides et acides nucléiques sur le spectre Raman. Nos travaux révèlent l’importance d’une modélisation statistique appropriée pour l’implémentation clinique de systèmes Raman chirurgicaux.----------ABSTRACT Because of its infiltrative nature and concealment behind the blood-brain barrier, primary brain cancer remains one of the most challenging oncological condition to diagnose and treat. The mainstay of treatment is maximal surgical resection. Raman spectroscopy has shown great promise to guide surgeons intraoperatively by identifying, in real-time, dense cancer regions that appear normal to the naked eye. The Raman signal of living tissue is, however, very challenging to interpret, and while most advances in Raman systems targeted the hardware, appropriate statistical modeling techniques are lacking. As a result, there is conflicting evidence as to which molecular processes are captured by Raman probes. This limitation hinders clinical translation and usage of the technology by the cancer-research community. This work focuses on the analytical aspect of Raman-based surgical systems. Its objective is to develop a robust data processing pipeline to confidently identify which molecular phenomena allow Raman systems to differentiate healthy brain and cancer during neurosurgeries. We first selected high-yield Raman regions based on previous literature on the subject, resulting in a list of reproducible Raman bands with high likelihood of brain-specific Raman signal. We then developed a peak-fitting algorithm to extract the shape (height and width) of the Raman signal at those specific bands. We described a mathematical model that accounted for all possible interactions between the selected Raman peaks, and the interaction between the peaks’ shape and the patient’s age. To validate the model, we compared its capacity to compress the signal while maintaining high information content against a Principal Component Analysis (PCA) of the Raman spectra, the fields’ standard. As a final step, we applied the feature engineering model to a dataset of intraoperative human Raman spectra to identify which molecular processes were indicative of brain cancer. Our method showed better information retention than PCA. Our analysis of in vivo Raman measurement showed that areas with high-density of malignant cells had increased expression of nucleic acids and protein compounds, notably collagen, tryptophan and phenylalanine. Patient age seemed to affect the impact of nucleic acids, proteins and lipids on the Raman spectra. Our work demonstrates the importance of appropriate statistical modeling in the implementation of Raman-based surgical devices

Learning Structure in Time Series for Neuroscience and Beyond

Advances in neuroscience are producing data at an astounding rate - data which are fiendishly complex both to process and to interpret. Biological neural networks are high-dimensional, nonlinear, noisy, heterogeneous, and in nearly every way defy the simplifying assumptions of standard statistical methods. In this dissertation we address a number of issues with understanding the structure of neural populations, from the abstract level of how to uncover structure in generic time series, to the practical matter of finding relevant biological structure in state-of-the-art experimental techniques. To learn the structure of generic time series, we develop a new statistical model, which we dub the probabilistic deterministic infinite automata (PDIA), which uses tools from nonparametric Bayesian inference to learn a very general class of sequence models. We show that the models learned by the PDIA often offer better predictive performance and faster inference than Hidden Markov Models, while being significantly more compact than models that simply memorize contexts. For large populations of neurons, models like the PDIA become unwieldy, and we instead investigate ways to robustly reduce the dimensionality of the data. In particular, we adapt the generalized linear model (GLM) framework for regres- sion to the case of matrix completion, which we call the low-dimensional GLM. We show that subspaces and dynamics of neural activity can be accurately recovered from model data, and with only minimal assumptions about the structure of the dynamics can still lead to good predictive performance on real data. Finally, to bridge the gap between recording technology and analysis, particularly as recordings from ever-larger populations of neurons becomes the norm, automated methods for extracting activity from raw recordings become a necessity. We present a number of methods for automatically segmenting biological units from optical imaging data, with applications to light sheet recording of genetically encoded calcium indicator fluorescence in the larval zebrafish, and optical electrophysiology using genetically encoded voltage indicators in culture. Together, these methods are a powerful set of tools for addressing the diverse challenges of modern neuroscience

Imaging studies of peripheral nerve regeneration induced by porous collagen biomaterials

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.There is urgent need to develop treatments for inducing regeneration in injured organs. Porous collagen-based scaffolds have been utilized clinically to induce regeneration in skin and peripheral nerves, however still there is no complete explanation about the underlying mechanism. This thesis utilizes advanced microscopy to study the expression of contractile cell phenotypes during wound healing, a phenotype believed to affect significantly the final outcome. The first part develops an efficient pipeline for processing challenging spectral fluorescence microscopy images. Images are segmented into regions of objects by refining the outcome of a pixel-wide model selection classifier by an efficient Markov Random Field model. The methods of this part are utilized by the following parts. The second part extends the image informatics methodology in studying signal transduction networks in cells interacting with 3D matrices. The methodology is applied in a pilot study of TGFP signal transduction by the SMAD pathway in fibroblasts seeded in porous collagen scaffolds. Preliminary analysis suggests that the differential effect of TGFP1 and TGFP3 to cells could be attributed to the "non-canonical" SMADI and SMAD5. The third part is an ex vivo imaging study of peripheral nerve regeneration, which focuses on the formation of a capsule of contractile cells around transected rat sciatic nerves grafted with collagen scaffolds, 1 or 2 weeks post-injury. It follows a recent study that highlights an inverse relationship between the quality of the newly formed nerve tissue and the size of the contractile cell capsule 9 weeks post-injury. Results suggest that "active" biomaterials result in significantly thinner capsule already 1 week post-injury. The fourth part describes a novel method for quantifying the surface chemistry of 3D matrices. The method is an in situ binding assay that utilizes fluorescently labeled recombinant proteins that emulate the receptor of , and is applied to quantify the density of ligands for integrins a113, a2p1 on the surface of porous collagen scaffolds. Results provide estimates for the density of ligands on "active" and "inactive" scaffolds and demonstrate that chemical crosslinking can affect the surface chemistry of biomaterials, therefore can affect the way cells sense and respond to the material.by Dimitrios S. Tzeranis.Ph. D

Ultrafast Energy Flow and Structural Changes in Nanoscale Heterostructures

A central goal of nanotechnology is the precise construction of nanoscale heterostructures with optimized chemical, physical or biological functionalities. It is known that function stems from structure but, in addition, function always involves nonequilibrium conditions and energy flow. The central topic of this thesis is the ultrafast energy flow in nanoscale heterostructures and how this energy flow drives ultrafast structural changes. The main experimental technique of this work is femtosecond electron diffraction, which probes the lattice response to electronic excitations. The nanoscale heterostructures contain metallic (Au) nanostructures of well-defined 0D or 2D morphology, supported on 2D substrates. In photoexcited heterostructures, thermal equilibrium is restored by electron-lattice interactions, within each component, and electronic and vibrational coupling across their interface. A newly developed model of ultrafast energy flow is used to measure the microscopic couplings, like electron-phonon coupling and interfacial vibrational coupling in nanoscale heterostructures using the observed Debye-Waller dynamics. Ultrafast energy flow in supported metallic nanostructures can initiate a rich variety of real-space motions like anharmonic lattice expansion and surface premelting, which manifest as distinct and quantifiable observables in reciprocal-space. These phenomena have been studied for Au nanoclusters on amorphous thin-film substrates. Au nanoclusters are found to exhibit ultrafast surface premelting at atypically low lattice temperatures and pronounced electron-lattice nonequilibrium conditions. Femtosecond electron diffraction is mostly used to study ultrafast motions related with phonons but in ultrasmall nanocrystals a new observable arises: the motion of the phonons’ frame of reference, meaning the crystal itself. This has been demonstrated for Au nanoclusters attached on graphene using femtosecond electron diffraction experiments, molecular dynamics and electron diffraction simulations. The substrate has a significant effect on the energy flow and the structural motions of ultrasmall, adsorbed nanostructures and, inversely, metallic nanostructures can alter fundamental properties of semiconducting substrates. Surface decoration with plasmonic, quasi-2D nanoislands of Au sensitizes WSe2 to sub-band-gap photons, causes nonlinear lattice heating and accelerates electron-phonon equilibration times. Conclusively, nanoscale heterostructures have a rich variety of nonequilibrium phenomena that affect their structure at ultrafast timescales. Ultrafast diffractive probes, like femtosecond electron diffraction, can provide a detailed, quantitative understanding of this relationship.In dieser Doktorarbeit wird der ultraschnelle Energietransfer in nanoskaligen Heterostrukturen sowie die dadurch verursachten ultraschnellen Strukturänderungen untersucht. Die wichtigste Methode dieser Arbeit ist Femtosekunden-Elektronenbeugung. Diese Methode untersucht die Reaktion des Kristallgitters auf elektronische Anregung. Die Heterostrukturen bestehen aus Gold-Nanostrukturen mit wohldefinierten 0D oder 2D Strukturen, die auf 2D Substraten aufgebracht sind. In mit Licht angeregten Heterostrukturen wird das thermische Gleichgewicht durch Elektron-Phonon-Kopplung in den einzelnen Materialien sowie durch elektronische und phononische Kopplung zwischen den Materialien wiederhergestellt. Ein neu eingeführtes Modell für ultraschnellen Energietransfer wird verwendet, um die ultraschnellen Veränderungen der Gittertemperatur zu beschreiben. Das Modell ermöglicht es, aus der gemessenen Debye-Waller-Dynamik mikroskopische Größen wie Elektron-Phonon-Kopplung und Phonon-Phonon-Kopplung an der Grenzfläche der nanoskaligen Heterostrukturen zu extrahieren. Ultraschneller Energietransfer in metallischen Nanostrukturen können eine Vielzahl an Veränderungen im Kristallgitter hervorrufen, z.B. Gitterausdehnung und Schmelzen der Kristalloberfläche. Diese Veränderungen gemessen werden, für 0D Gold Nanostrukturen die auf 2D Substraten aufgebracht sind. Au-Nanocluster zeigen ultraschnelles Schmelzen der Kristalloberfläche bei außergewöhnlich niedrigen Gittertemperaturen und ausgeprägtem Nichtgleichgewichtszustand zwischen Elektronen und Gitter. Femtosekunden Elektronen Beugung ist eine Methode, die am häufigsten bei der Untersuchung durch Phonen induzierter ultraschneller Bewegungen von Atomen Anwendung findet. In ultrakleinen Nanokristallen stellt sich aber ein neue Herausforderung dar: der Referenzrahmen der Bewegung der Phononen, was der Kristall selber ist. Demonstriert wurde das für Gold 0D Nanostructuren, die auf Graphen. Das Substrat hat einen signifikanten Einfluss auf den Energiefluss und die strukturelle Bewegung von ultrakleinen, adsorbierten Nanostrukturen und in inverser Weise können metallische Nanostrukturen due fundamentalen Eigenschafter halbleitender Proben verändern. Wenn WSe2 mit plasmonische quasi-2D Gold-Nanoinseln bedeckt wird, ändern sich dessen Eigenschaften so, dass Photonen unterhalb der Bandlücke absorbiert werden können. Die resultierende Erwärmung des Gitters folgt einem nichtlinearen Zusammenhang mit der Fluenz des einkoppelnden Lasers und die Elektron-Gitter Relaxationszeit ist reduziert

In vivo Analysis and Modeling Reveals that Transient Interactions of Myosin XI, its Cargo, and Filamentous Actin Overcome Diffusion Limitations to Sustain Polarized Cell Growth

Tip growth is a ubiquitous process throughout the plant kingdom in which a single cell elongates in one direction in a self-similar manner. To sustain tip growth in plants, the cell must regulate the extensibility of the wall to promote growth and avoid turgor-induced rupture. This process is heavily dependent on the cytoskeleton, which is thought to coordinate the delivery and recycling of vesicles containing cell wall materials at the cell tip. Although significant work has been done to elucidate the various molecular players in this process, there remains a need for a more mechanistic understanding of the cytoskeletonÂ’s role in tip growth. For this reason, specific emphasis should be placed on understanding the dynamics of the cytoskeleton, its associated motors, and their cargo. Since the advent of fluorescence fusion technology, various quantitative fluorescence dynamics techniques have emerged. Among the most prominent of these techniques is fluorescence recovery after photobleaching (FRAP). Despite its prominence, it is unclear how to interpret fluorescence recoveries in confined cellular geometries such as tip-growing cells. Here we developed a digital confocal microscope simulation of FRAP in tip-growing cells. With this simulation, we determined that fluorescence recoveries are significantly influenced by cell boundaries. With this FRAP simulation, we then measured the diffusion of VAMP72-labeled vesicles in the moss Physcomitrella patens. Using finite element modeling of polarized cell growth, and the measured VAMP72-labeled vesicle diffusion coefficient, we were able to show that diffusion alone cannot support the required transport of wall materials to the cell tip. This indicates that an actin-based active transport system is necessary for vesicle clustering at the cell tip to support growth. This provides one essential function of the actin cytoskeleton in polarized cell growth. After establishing the requirement for actin-based transport, we then sought to characterize the in vivo binding interactions of myosin XI, vesicles, and filamentous actin. Particle tracking evidence from P. patens protoplasts suggests that myosin XI and VAMP72-labeled vesicles exhibit fast transient interactions. Hidden Markov modeling of particle tracking indicates that myosin XI and VAMP72- labeled vesicles move along actin filaments in short-lived linear trajectories. These fast transient interactions may be necessary to achieve the rapid dynamics of the apical actin, important for growth. This work advances the fieldÂ’s understanding of fluorescence dynamics, elucidates a necessary function of the actin cytoskeleton, and provides insight into how the components of the cytoskeleton interact in vivo